BIODS 271: Foundation Models for Healthcare

Stanford University, Winter 2024

Links

Course Description

Generative AI and large-scale self-supervised foundation models are poised to have a profound impact on human decision making across occupations. Healthcare is one such area where such models have the capacity to impact patients, clinicians, and other care providers. In this course, we will explore the training, evaluation, and deployment of generative AI and foundation models, with a focus on addressing current and future medical needs. The course will cover models used in natural language processing, computer vision, and multi-modal applications. We will explore the intersection of models trained on non-healthcare domains and their adaptation to domain-specific problems, as well as healthcare-specific foundation models.

Prerequisites

Familiarity with machine learning principles at the level of CS 229, 231N, or 224N

Time & Location

Mon, Wed 3:00 PM - 4:20 PM at Alway Building, Room M112

Instructors

Akshay Chaudhari

akshaysc@stanford.edu

Office Hours: Mondays 4:30-5:30pm @ Lucas Center, Room P080 (1201 Welch Road). From the outside of the building go downstairs one level, cross the glass bridge, and it should be first room on the right once you enter. Zoom office hours by appointment.

James Zou

jamesz@stanford.edu

Office Hours: After class and by appointment

Tanveer Syeda-Mahmood

stf@us.ibm.com

Office Hours: Mondays 4:30 - 6:30 pm @ MSOB Room 393 (No office hours on March 6th)

TAs

Kevin Wu

wukevin@stanford.edu

Office Hours: Wednesdays 2-3pm @ Packard 3rd floor kitchenette area

Maya Varma

mvarma2@stanford.edu

Office Hours: Mondays 6-7pm @ Zoom (see Ed for link)

Coursework

Coursework will be divided into the following categories:

Class Participation (10%): Students are encouraged to attend lectures and participate actively by asking questions and offering comments.
Homework Assignments (30%): This course will include two homework assignments. Additional details coming soon.
Final Project (60%): Students will form teams and choose from one of the suggested projects or select their own project. Teams are expected to work on the research project throughout the second half of the quarter and produce conference-style papers. Each team will present the paper to the entire class at the end of the semester. Additional details coming soon.